Dept. of Computer Sci. & Eng., The Chinese University of Hong Kong 1 Cloud Computing A Trend Taking Shape Yangfan Zhou Dept. Computer Science and Engineering

Dept. of Computer Sci. & Eng., The Chinese University of Hong Kong 1

Cloud ComputingA Trend Taking Shape

Yangfan ZhouDept. Computer Science and Engineering

The Chinese University of Hong Kong


Contents

Drive of cloud computing

Nature of cloud computing

Cloud computing industry

Conclusions

Trustworthy cloud computing


Contents




Conclusions



What the buzzword is about

You may ask: Is cloud computing• created by marketing people?? A new name of an ol

d and awful technology??• a new technology focusing on higher-speed comput

ing??• a new technology that can let you use more storage

capacity??• a new concept that can help programmers? maintai

ners?

Let’s see what it is via its economic drive


Hosting computing systems

CSE co. ltd.

ServerServer



CSE co. ltd.



CSE co. ltd.



CSE co. ltd.

Peak hourMost of the time

Waste of resource = waste of $



• You pay for the servers no matter whether you are using them or not– The money you spend to buy the hardware – The salary for the maintainers– The electricity (including that for air conditionin

g)– Other maintenance costs, e.g., repairing, upgradi

ng, renting a room for the servers.

Waste of resource = waste of $

CSE co. ltd.



CSE co. ltd.

ServerServer

How I wish this cloud can really host the computing systems for me

So that I can provide a scalable, on-demand service.


When there is a dream, there is a market

• A lot of companies said: Sure!!! We can produce such a cloud for you– Microsoft, IBM, Google, Amazon, HP, Yahoo!, Intel..

• A lot of people do have done a lot of hard work – Many more IT companies join and make the same promise befor

e they really know what the promise means technologically– Try to define “cloud computing”– Try to register “cloud computing” as a trademark– Found many startups

I don’t have to care what it is. I can just host my services here.

marketing


When there is a dream, there is a marketA recent reaction in the capital market

PE=Price per Share/Earning per Share


When there is a dream, there is a market


Contents




Conclusions



Cloud computing preliminaries

Computing System

Application

Platform

Infrastructurehardware

OS, middleware

User software, API


Cloud computing preliminaries

Application

Platform

Infrastructure

SaaS: Software as a service

PaaS: Platform as a service

Provided by the cloud computing provider IaaS: Infrastructure as a se

rvice

A cloud user’s own application and OS

A cloud user’s own application

Three cloud computing stacks


Out-sourcing is a great idea!!!

Pay as you go

The core of cloud computing is computing/storage outsourcing

vs

Pay for exactly what you’ve used

Pay as you go!!

Cost Down!!!


OutsourcingElectronics industry

Brand vendor

Manufacturer

Manufacturing outsourcing

Different manufacturing requirements in low season and high season

# of product lines# of workers

OEM: original equipment manufacturer ODM: original design manufacturer

Cost down!!!

Service vendor

Cloud computing provider

Computing and storage outsourcing

Different service requirements in different times

# of servers# of maintainers

OSP: original service provider

Cost down!!!

Services for end users


Cloud computing v.s. Grid computingCloud computing is not just a new term for an old idea• Grid computing

– Focus on let more than one computer coordinate to solve a problem together

– Often include heterogeneous environments, i.e., computers of different capacity, configuration, and even different OS.

– Business Model: project-oriented • Cloud computing

– An application doesn't access resources directly. It accesses them through a service. Usually the service has access to a large amount of physical resources, and can dynamically allocate them on demand.

– Business Model: pay on a consumption basis

A cloud can use grid computing technique. But a grid is not necessarily a cloud or part of a cloud.


Contents




Conclusions




• Amazon Elastic Compute Cloud (EC2)• Google App Engine• Microsoft's Windows Azure Platform • Other small startups: Heroku & Engine Yard

Representative Providers


Amazon Elastic Compute Cloud (EC2)• Xen-based virtual computing environment where a user ca

n run Linux-based applications• A user can boot an Amazon Machine Image to create a virt

ual machine instance containing any software desired• IaaS: A user can control nearly the entire software stack, fr

om the kernel upwards. It provides low level of virtualization– raw CPU cycles, block-device storage, IP-level connectivity

• Provided together with Simple Storage Services (S3)– data storage service

• A user can increase or decrease capacity within minutes• Amazon charges $0.084/hour ($61/month) for the smalles

t virtual machine and around $0.10 per gigabyte of data transfer


Google App Engine• Application domain-specific platforms. Not suitable for g

eneral-purpose computing• Allow a user to run Web applications in Python or Java. H

ost applications in Google-managed data centers• Enforcing an application structure of clean separation bet

ween a stateless computation tier and a stateful storage tier

• Support APIs for the Google Datastore, Google Accounts, URL fetch, image manipulation, and email services

• Automatically scale in response to load increases and decreases, and users are charged by the cycles used

• Free up to a certain level of used resources– 500MB of storage and about 5 million page views


Microsoft's Windows Azure Platform• Use Windows Azure Hypervisor as the infrastructure• Use the .NET framework as the application container and

applications are compiled to the Common Language Runtime, a language-independent managed environment

• PaaS: Can be considered as something intermediate between application frameworks like Google App Engine and hardware virtual environments like Amazon EC2

• Support general-purpose computing, rather than a single category of application

• Users can choose language, but cannot control the underlying operating system or runtime

• The libraries provide a degree of automatic network configuration and failover/scalability, but require the developer to declare some application properties


Other small startups: Heroku & Engine Yard• Heroku

– Based on Ruby on Rails– Dyno: a dyno is a single process running ruby code

on a server in Heroku grid– Application codes run inside the dyno grid, occupy

ing as many slots as needed. New dynos for an application can be started in under 2 seconds for most applications.

– Dynos are launched in an environment containing your app's database and cache information

– Charge on a capacity basis. Free for the entry level• Engine Yard

– Also based on Ruby on Rails– Similar


Contents




Conclusions



Cloud computing challenges and opportunities

Challenges and opportunities of our interests• How to provide a scalable service, adaptive to the divers

ity of end-users (# of end users, locations of end users)– QoS provisioning

• How to provide a secure service– Privacy preserving– Secure access guaranteeing– Resilient to attacks

• How to provide a reliable service– Fault tolerance– New test methodologies

Why people will trust their data/computing to the cloud and accept the idea of computing/storage outsourcing??

Towards Trustworthy Cloud Computing



Topic 1: QoS ProvisioningHow to provide a scalable service, adaptive to the diversity of end-users


• Preliminaries of service composition – A service can be composed by many

components– Components may locate world-wide – There are many components with the same

functionalities


`A cloud service

components


Potential research problemsQoS driven recommendation for cloud service

composition • Observations

– The QoS properties of the same cloud service component are different in the users’ perspective

• Users are located world wide• The network connections of different users are different

– Software quality metrics no longer depends only on the software per se



Cloud computing challenges and opportunitiesPotential research problems• Problem 1: QoS driven recommendation for cloud

service composition: How to recommend a set of components to form a service based on a cloud user’s networking feature– Heuristic: recommend based on IP address– Recommend based on the similarity– Mapping our Web services research to this domain

Component 1

Component 2

Component 3

Component 4

User 1 Value 11 Value 12 Value 13 Value 14



New User Value 43 Value 44Prediction Prediction


Potential research problems• Problem 2: How many resources/components that shoul

d be proactively reserved to guarantee QoS– Based on a prediction of the service usage – We should look into how resources are virtualized



Potential research problems• Problem 3: Real-world measurement on the existing clou

ds – Amazon EC2, Google APP Engine– Elastic? – QoS all over the world?


Commercial Cloud

PlanetLab Node



Topic 2: Fault tolerance and reliabilityHow to provide a reliable service given the fact no components are perfectly created, and the Internet is unpredictable


Cloud computing challenges and opportunitiesPotential research problem• Problem 1: Reliability prediction of cloud service

composition, i.e., how to predict the reliability quality of a service that is composed by a set of components– Previous method: measurement

• Cannot know before deployment• Cannot know if we cannot do tremendous tests• Hard to know if we don’t know how components are coded

– Predict also based on the similarity

– Reliability can be predicted in the service design phase based on its composition specific

Component 1

Component 2

Component 3

Component 4




New User Value 43 Value 44Prediction Prediction



Potential research problem• Problem 2: How to tolerate faults in stateful cloud applic

ations. – Long term business process– Cloud computing: Redundancy in nature– Solutions

• Recovery– Check pointing and rollback

• Standby redundancy– State transfer

• Parallel redundancy– State synchronization.



Other potential research problems• How to exploit the redundancy in cloud computing• Cloud failure detection• Component dependency analysis• Error propagation analysis



Topic 3: SecurityHow to provide a secure service


Potential research problems• Problem 1: Identify critical cloud nodes

– Dynamic service composition Complex component invoking relations

– Reliability and attack-resilience features of different components may have different impacts on the whole cloud

– Identify critical components • Purpose: To reduce the vulnerability• Approach: based on the service composition relatio

ns (Ranking)



Potential research problems• Problem 2: Attack resilient

– Subproblem 1: DoS masking• Utilize the redundant nature of components• Intelligent watchdog technique• Load balance technique

– Subproblem 2: Anti-Masquerade attacks • A malicious node will fake a large number of pseudo

nymous components, which can be used to gain a biased trust in service composition

• Design a reputation mechanism• Use similar techniques as those for anti-spam in sea

rching engines



Potential research problems• Problem 3: New access control paradigm

– Purpose:• Anti-freeriding• Guarantee data security

– Challenge: How to share the user credentials among different cloud nodes/components efficiently• A user may login at a node • And invoke a component at another node • Access control paradigm for stateful services




Email serversYou put your emails there without worrying about leaking your privacy?

We have laws and commercial moral.

- Can still be a good area to publish academic papers- Several top conference papers

Potential research problem?• Privacy preserving

– There are already papers in this area.– May not be a big deal, because …– We may have to provide private data to the cloud because we n

eed it to compute them

Why we trust the IT guys in our department more than a cloud computing company far far away



Topic 4: TestingHow to test cloud computing system


Potential research problems• Problem 1: Embracing user-supplied software testing

– Test-as-you-use paradigm is possible

– Tremendous runtime data can greatly facilitate testing


Software is running at servers, runtime data can be collected easily

Software is running at PCs, runtime data can only be collected by user report


Potential research problems• Problem 2: Anomaly detection (for locating potential faul

ts) – Test-as-you-use paradigm is possible– There are tremendous runtime data– Can these data be utilized for locating potential faults

• How to locate anomaly• How to locate the root cause of the anomaly




Topic 5: SystemHow to build a cloud computing system


Potential research problemsProblem 1: Building a cloud• An organization provides its PCs as nodes in the cloud• Users can use the cloud• Challenges

– Many engineering efforts• Which language(s) it supports• The framework: IaaS or PaaS• More including coding

– Accounting system– Reliability system– QoS provisioning– Attack-resilient– And many others



Potential research problemsProblem 2: Building an open cloud• Everyone can provide his/her PC as a node in the cloud• And everybody can use the cloud• Challenges

– Many engineering efforts• Which language(s) it supports• The framework: IaaS or PaaS• More including coding

– A reputation mechanism to stimulate contributions– A reputation mechanism for anti-freeriding– Attack-resilient



• Task 1: Achieving trustworthy cloud computing in architectural design – Ranking-based identification of critical cloud nodes– QoS driven recommendation for cloud service composition – Development of an open cloud computing architecture

• Task 2: Enhancing the security and reliability for cloud computing paradigm– A new access control paradigm – Tolerating faults in cloud computing

– Designing a reputation mechanism for an open cloud

• Task 3: Formulating a new testing methodology for trustworthy cloud computing– Embracing user-supplied software testing in cloud computing paradigm– Unveiling faults via symptom mining

• Task 4: Implementing an open-source cloud computing framework – Test-bed construction – Large scale experiments

Current proposal outline


Conclusions

1. Cloud computing include IaaS, PaaS, SaaS2. We identify several research areas

QoS provisioningFault tolerance and reliabilitySecurityTestingCloud system implementations

3. Cloud service composition is a critical feature, enriching QoS provisioning, fault tolerance, and reliability issues

4. Redundancy is an important nature of cloud computing systems, enabling fault tolerance, attack resilience

5. Application runtime data are available in the cloud, facilitating reliability prediction, QoS provisioning, and testing

Documents

Dept. of Computer Sci. & Eng., The Chinese University of Hong Kong 1 Cloud Computing A Trend Taking Shape Yangfan Zhou Dept. Computer Science and Engineering